A Robotic Approach towards Quantifying Epipelagic Bound Plastic Using Deep Visual Models
This addresses the cost-intensive and laborious manual sampling issue in marine plastic quantification, offering a more efficient solution for environmental monitoring and cleanup efforts.
The study tackled the problem of quantifying marine plastic debris by developing an autonomous method using neural networks and computer vision models, achieving a Mean Average Precision of 85% and an F1-Score of 0.89 with near real-time processing speeds of ~2 ms per image.
The quantification of positively buoyant marine plastic debris is critical to understanding how plastic litter accumulates across the world's oceans and is also crucial to identifying hotspots for targeted cleanup efforts. Currently, the most common method to quantify marine plastic is using manta trawls for manual sampling. However, this method is cost-intensive and requires human labor. This study removes the need for manual sampling by using an autonomous method using neural networks and computer vision models, which trained on images captured from various layers of the ocean column to perform real-time plastic quantification. The best performing model has a Mean Average Precision of 85% and an F1-Score of 0.89 while maintaining near real-time processing speeds ~2 ms/img.